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Gesture recognition in streaming motion data using offline training with a limited training set

Franssen, Tim K.C. (2013) Gesture recognition in streaming motion data using offline training with a limited training set.

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Abstract:This Master's thesis is about the analysis of motion capture data, focussing on quickly and accurately recognizing arm gestures for use in a virtual infantry training system. We do a comparative study between the SVM and HMM classi�cation approaches, di�erent features (coordinates, motion vectors, a combination of both) and parameters (motion vector o�set, cost, gamma, number of states et cetera) that are speci�c to the application of a training simulation. We show that gesture classi�cation can be used in a virtual infantry training situation. Less than ten minutes of training data from one instructor is su�cient for classifying nine di�erent gestures from students with an f-measure of 0.65 on average. This classi�cation can be used for a plethora of applications including scoring students relative to each other, allowing the instructor gesture control over the scenario and as input to arti�cial intelligent agents.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Human Media Interaction MSc (60030)
Link to this item:http://purl.utwente.nl/essays/62895
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